• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.5 no.2 s.10
• /
• pp.1-10
• /
• 2006

In this paper, VTS(Vector Taylor Series) algorithm, which was proposed by Moreno at Carnegie Mellon University in 1996, is analyzed and simulated. VTS is considered to be one of the robust speech recognition techniques where model parameter conversion technique is adapted. To evaluation performance of the VTS algorithm, We used CMN(Cepstral Mean Normalization) technique which is one of the well-known noise processing methods. And the recognition rate is evaluated when white gaussian and street noise are employed as background noise. Also, the simulation result is analyzed in order to be compared with the previous one which was performed by Moreno.

• Speech Sciences
• /
• v.10 no.3
• /
• pp.251-261
• /
• 2003

There has been many research efforts to overcome the problems of speech recognition in noisy conditions. Among the noise-robust speech recognition methods, model-based adaptation approaches have been shown quite effective. Particularly, the PMC (parallel model combination) method is very popular and has been shown to give considerably improved recognition results compared with the conventional methods. In this paper, we experimented with the VTS (vector Taylor series) algorithm which is also based on the model parameter transformation but has not attracted much interests of the researchers in this area. To verify the effectiveness of it, we employed the algorithm in the continuous density HMM (Hidden Markov Model). We compared the performance of the VTS algorithm with the PMC method and could see that the it gave better results than the PMC method.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.4
• /
• pp.285-289
• /
• 2012

In a coastal area, all of the vessels are always exposed to the potential risk, taking into the maritime accident statistics account over the last decades. To manage vessels underway safety, the characteristics of ship movements in a fairway should be recognized by VTS system or VTS operators. The IMO has already mandated the shipboard carriage of AIS since 2004, as stated in SOLAS Chapter V Regulation 19. As a result, the static and dynamic information of AIS data has been collected for vessel traffic management in the coastal areas and used for VTS. This research proposes a simple algorithm of recognizing potentially risky ships by observing their trajectories on the fairway. The static and dynamic information of AIS data are collected and the curvature for the ship trajectory is surveyed. The proposed algorithm finds out the irregularity of ship movement. The algorithm effectively monitors the change of navigation pattern from the curvature analysis of ship trajectory. Our method improves VTS functions in an intelligent way by analyzing the navigation pattern of vessels underway.

• Journal of Navigation and Port Research
• /
• v.37 no.4
• /
• pp.337-343
• /
• 2013

To ensure the safety for vessels anchored in stormy weather, duty officer and VTS operator have to frequently check whether their anchors are dragging. To judge dragging of the anchored vessel, it is important for VTS operator to recognize the turning circle and its center of the anchored vessel. The judgement for the anchored vessel dragging can be made by using Radar and AIS. If it is available, CCTV or eye-sighting can be used to know the center of turing circle. However, the VTS system collects individual ship's dynamic information from AIS and ARPA radar and monitors of the anchored vessels, it is difficult for VTS operator not only to get the detailed status information of the vessels, but also to know the center of turning circle. In this study, we propose an efficient algorithm to estimate the center of turning circle of anchored vessel by using the ship's heading and position data, which were from AIS. To verify the effectiveness of the proposed algorithm, the experimental study was made for the anchored vessel under real environments.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.1103-1106
• /
• 1999

Telegram is an indispensible information & telecommunication system to our daily life. VTS(Voice Telegram System) under intensive research is intended to enhance exchanging capability of information & telecommunication by adding voice media to existing telegram system. Overall configuration and necessary core technologies of the system were investigated for its development. Among those many technologies in need, the technology of compressing and recording data is most critical to the development of cheap hardware. This is so called vocoder algorithm and is the core technology of voice information system. So, here, vocoder algorithm now being studied will be introduced.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2015.10a
• /
• pp.47-48
• /
• 2015

VTSO make a decision which one will be danger and what to expect ship's actions due to each encountering situation with CPA(Closest Point of Approach) and TCPA(Time to Closest Point of Approach) by monitoring ship's vectors(Course & Speeds) in real-time through the VTS system. This study is the fundamental research for developing algorithm and system that does not decide the collision risk in one's own ship's viewpoints, but it identifies the related ships into danger through the third party(VTS ) in real time.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2023.05a
• /
• pp.40-41
• /
• 2023

This paper introduces the design of detection, recognition, and tracking algorithms for VTS radar image-based objects. The detection of objects in radar images utilizes artificial intelligence technology to determine the presence or absence of objects, and can classify the type of object using AI technology. Tracking involves the continuous tracking of detected objects over time, including technology to prevent confusion in the movement path. In particular, for land-based radar, there are unnecessary areas for detection depending on the terrain, so the function of detecting and recognizing vessels within the region of interest (ROI) set in the radar image is included. In addition, the extracted coordinate information is designed to enable various applications and interpretations by calculating speed, direction, etc.

• Journal of the Institute of Convergence Signal Processing
• /
• v.15 no.2
• /
• pp.37-41
• /
• 2014

The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.5
• /
• pp.866-874
• /
• 2006

This paper proposes a new Dynamic Voltage Scaling(DVS) algorithm to achieve low-power scheduling of aperiodic hard real-time tasks. Aperiodic tasks schedulingcannot be applied to the conventional DVS algorithm and result in consuming energy more than periodic tasks because they have no period, non predictable worst case execution time, and release time. In this paper, we defined Virtual Periodic Task Set(VTS) which has constant period and worst case execution time, and released aperiodic tasks are assigned to this VTS. The period and worst case execution time of the virtual task can be obtained by calculating task utilization rate of both periodic and aperiodic tasks. The proposed DVS algorithm scales the frequency of both periodic and aperiodic tasks in VTS. Simulation results show that the energy consumption of the proposed algorithm is reduced by 11% over the conventional DVS algorithm for only periodic task.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.253-256
• /
• 2005

The use of satellite remote sensing in maritime safety and security can aid in the detection of illegal fishing activities and provide more efficient use of limited aircraft or patrol craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which use the ground-based radar system have some difficulties in detecting moving ships due to the limited detection range. A virtual vessel traffic control system is introduced to contribute to prevent a marine accident such as collision and stranding from happening. Existing VTS has its limit. The virtual vessel traffic control system consists of both data acquisition by satellite remote sensing and a simulation of traffic environment stress based on the satellite data, remotely sensed data. And it could be used to provide timely and detailed information about the marine safety, including the location, speed and direction of ships, and help us operate vessels safely and efficiently. If environmental stress values are simulated for the ship information derived from satellite data, proper actions can be taken to prevent accidents. Since optical sensor has a high spatial resolution, JERS satellite data are used to track ships and extract their information. We present an algorithm of automatic identification of ship size and velocity. This paper lastly introduce the field testing results of ship detection by RADARSAT SAR imagery, and propose a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.